Data Engineering Courses in India Compared (2026)

Short answer: AI systems are only as good as the data pipelines under them, which is why more data engineering courses now add GenAI. When you compare courses, check three things:

1. Is it a dedicated data engineering course, or data science with some pipelines?
2. Which stack do you actually build on: Azure, AWS, Databricks, Snowflake, PySpark, Kafka, Airflow, dbt?
3. Does the GenAI part run on real data platforms (RAG over your data, agents using data tools), or is it a separate add-on?

Side-by-side comparison

GUVI's live (Zen Class) course list did not include a dedicated data engineering program on 29 October 2026, so the GUVI options above are the closest ones we found. If that changes, we'll update this guide.

Who each option is for

• GUVI's free Data Engineering course suits anyone who wants a no-cost introduction to how data pipelines, warehouses and data lakes fit together.
• GUVI's Data Science & GenAI program suits learners whose goal is data science and ML rather than building pipelines, and who want teaching in several Indian languages.
• Intellipaat's program suits learners who want a longer program with an MITx MicroMasters link and a large self-paced library alongside live sessions.
• Scaler's program suits people who want a long, broad data science program in which data pipelines are one part.
• Datamarcos's Advance Data Engineering program suits analysts, BI developers, ETL engineers, backend developers and DBAs who already know SQL. It's for people who want to build cloud pipelines first (Azure, AWS, Databricks) and then put RAG and agents on top of that data. If you've never written SQL, take a free SQL course first, because SQL is the starting point.

Data engineer, AI engineer, or both?

Our view: data engineering skills keep their value whatever happens with AI, and adding GenAI on top opens more kinds of work than either skill alone. That's a judgement, not a guarantee.

Questions to ask any data engineering course

1. Which cloud do I build on, and do I get a real environment?
2. Do I build both batch and streaming pipelines? (Streaming usually means something like Spark Structured Streaming or Kafka.)
3. Is data quality (tests and expectations, for example with Great Expectations) part of the projects?
4. Is there orchestration with Apache Airflow, ADF or Databricks Workflows?
5. Is the GenAI part built on the pipeline data, or is it a separate demo?
6. What data does the capstone take in, and what does it deliver?

Where Datamarcos fits

The Advance Data Engineering with Applied GenAI & Agentic AI program runs in two tracks.

Track 1: a 4-month foundation

• Month 1: Python and SQL refresher, data concepts, Azure Data Lake
• Month 2: PySpark, Azure Data Factory pipelines, Delta Lake and orchestration
• Month 3: AWS data services, Databricks lakehouse, Spark Structured Streaming, Great Expectations and dbt-style transformations
• Month 4: an end-to-end data engineering capstone and an introduction to GenAI data applications

Track 2: an advanced specialisation in four modules: applied GenAI foundations, retrieval-augmented generation, agentic AI systems (LangGraph, CrewAI) and enterprise AI platforms (Snowflake Cortex AI, Databricks Mosaic AI), ending in an integrated agentic data application. Admissions confirms the cohort schedule for this track.

Sessions are live and mentor-led. We don't guarantee placement, salary or a hike.

Already a data engineer and want to focus on LLM applications? See the Generative AI & Agentic AI Engineer program. Training a data team? See corporate training. More comparisons are in the guides hub.